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on Technology and Industrial Dynamics |
| By: | Nicholas A. Carollo; Elior Cohen; Jingyi Huang |
| Abstract: | Using novel occupational data from the United States between 1860 and 1940, we evaluate Adam Smith’s core propositions regarding the division of labor, market size, innovation, and productivity. We document significant growth in occupational diversity during this period using new measures of labor specialization that we construct from workers’ self-reported job titles in the decennial census. Consistent with Smith’s hypotheses, we find strong empirical evidence that labor specialization increases with the extent of the market, is facilitated by technological innovation, and is ultimately associated with higher manufacturing productivity. Our findings also extend Smith’s narrative by highlighting the role of organizational changes and innovation spillovers during the Second Industrial Revolution. These results speak to the enduring relevance of Smith’s insights in the context of an industrializing economy characterized by large firms, complex organizational structures, and rapid technological change. |
| Keywords: | division of labor; occupations; productivity growth; technological change |
| JEL: | N11 O14 J24 D24 |
| Date: | 2025–09–03 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedkrw:101725 |
| By: | Philippe Aghion; Antonin Bergeaud; Timo Boppart; Peter J. Klenow; Huiyu Li |
| Abstract: | Firm price-cost markups may reflect (a) bigger step sizes from quality innovations that confer significant knowledge spillovers onto other firms, and/or (b) higher process efficiency than competing firms or other factors which bear no obvious knowledge externality. We write down an endogenous growth model with innovation step size and process efficiency as alternative sources of markup heterogeneity. Compared with the laissez-faire equilibrium, the social planner wants to reallocate research towards high step size firms but not high process efficiency firms. We then use price and productivity data across firms in French manufacturing to infer firm step sizes and process efficiency. We find that the planner could achieve faster growth by reallocating research toward high step size firms, and more so if high step size firms could freely license their innovations to high process efficiency firms. |
| JEL: | O31 O38 O41 O52 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34190 |
| By: | Gavin Wang; Lynn Wu |
| Abstract: | Although AI has the potential to drive significant business innovation, many firms struggle to realize its benefits. We examine how the Lean Startup Method (LSM) influences the impact of AI on product innovation in startups. Analyzing data from 1, 800 Chinese startups between 2011 and 2020, alongside policy shifts by the Chinese government in encouraging AI adoption, we find that companies with strong AI capabilities produce more innovative products. Moreover, our study reveals that AI investments complement LSM in innovation, with effectiveness varying by the type of innovation and AI capability. We differentiate between discovery-oriented AI, which reduces uncertainty in novel areas of innovation, and optimization-oriented AI, which refines and optimizes existing processes. Within the framework of LSM, we further distinguish between prototyping focused on developing minimum viable products, and controlled experimentation, focused on rigorous testing such as AB testing. We find that LSM complements discovery oriented AI by utilizing AI to expand the search for market opportunities and employing prototyping to validate these opportunities, thereby reducing uncertainties and facilitating the development of the first release of products. Conversely, LSM complements optimization-oriented AI by using AB testing to experiment with the universe of input features and using AI to streamline iterative refinement processes, thereby accelerating the improvement of iterative releases of products. As a result, when firms use AI and LSM for product development, they are able to generate more high quality product in less time. These findings, applicable to both software and hardware development, underscore the importance of treating AI as a heterogeneous construct, as different AI capabilities require distinct organizational processes to achieve optimal outcomes. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2506.16334 |
| By: | L. Serafini; R. Paci; E. Marrocu |
| Abstract: | This paper investigates the impact of green, digital, and twin transition investments on firm performance in Italy during the 2014-2020 programming period. Drawing on detailed project-level data from the OpenCoesione platform on ERDF-funded initiatives, we classify investments according to their thematic focus and apply a staggered Difference-in-Differences approach to estimate their effects on value added, employment, and labour productivity. Our results show that firms supported through twin transition projects, those combining green and digital components, achieve the most substantial and sustained gains in value added and productivity. These integrated interventions appear particularly effective in enhancing firm performance and capacity utilisation, with employment effects emerging more gradually. Purely green and digital projects also yield positive outcomes, though with more moderate and variable effects. We further document significant heterogeneity across regions and sectors, with stronger impacts observed among firms located in Northern and Southern Italy and in knowledge-intensive sectors. Our findings highlight the importance of strategic investment design - transition-oriented and multi-dimensional projects consistently outperform single-focus initiatives. These results suggest that EU cohesion policy plays a pivotal role in supporting structural transformation, particularly when funding is targeted to integrated projects that align with broader environmental and digital policy goals. |
| Keywords: | Twin Transition;Green policies;Digital policies;Innovation and firm Performance;Cohesion Policy;Counterfactual Impact Analysis |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:cns:cnscwp:202511 |
| By: | Gustavo de Souza |
| Abstract: | I use administrative data on artificial intelligence (AI) software created in Brazil to study its effects on the labor market. Owing to a unique copyright system, Brazilian firms have registered their software with the government since the 1980s, creating a detailed record of nearly all commercial AI applications developed in the country. Drawing on this registry, I show that AI is widely used not only in administrative tasks but also in production settings, where it primarily supports process optimization and quality control. Using an instrument based on variation in software development costs, I find that AI affects administrative and production workers differently. Among office workers, AI reduces employment and wages, particularly for middle-wage earners. Among production workers, it increases employment of low-skilled and young workers operating machinery. These results suggest that AI displaces routine office tasks while making machines more productive and easier to operate, leading to a net increase in employment. |
| Keywords: | Artificial intelligence; Automation; software; inequality |
| JEL: | J23 J24 F63 |
| Date: | 2025–07–21 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedhwp:101714 |
| By: | Mantej Pardesi; Frank Corvers; Harald Pfeifer |
| Abstract: | We study how investment spikes in technologies and complementary infrastructure influence firms' hiring and training strategies. While prior work emphasizes how technologies reallocate skill demand, few focus on how firms acquire the required skills. Using linked employer-employee data on German establishments, we identify spikes by their technological composition and capital vintages. Event study estimates show that investment spikes in ICT and production line technologies lead to an upscaling effect raising employment by external hiring followed by training of young apprentices. Combining technologies with factories and plants induces firms to use apprenticeship training without an increase in external hiring. Incumbent workers are trained when investment spikes renew the vintage of firm's capital. Our findings support a vintage human capital framework in which technology adoption induces firms to gradually adjust workforce through hiring and training while preserving expertise of incumbent workers. |
| Keywords: | investment spikes, technology adoption, technology vintages, training, skill formation |
| JEL: | J24 D22 O33 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:iso:educat:0249 |
| By: | Arenas-Arroyo, Esther (Vienna University of Economics and Business); Fabian, Jacob; Mengel, Friederike (University of Essex); Schmidpeter, Bernhard (Vienna University of Economics and Business); Serafinelli, Michel (King's College London) |
| Abstract: | How does firms' skill demand change as the business landscape evolves? We present evidence from the green transition by analyzing how hurricanes impact demand for green skills. These disasters signal the risks of not acting on environmental issues. Using data from U.S. online job postings (2010--2019) and hurricane paths, we create a new measure of green job postings. Firms in areas affected by hurricanes are 6.4\% more likely to post jobs that require green skills after the event, particularly those serving local markets. |
| Keywords: | online job postings, green transition, green skills, hurricanes |
| JEL: | J23 Q54 L20 J24 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18102 |
| By: | Nida Çakır Melek; Elior Cohen |
| Abstract: | Understanding how occupations differ in their exposure to emissions-intensive activities is fundamental for analyzing labor market risks amid changes in the energy mix. We develop new, data-driven measures of occupational emissions intensity that capture heterogeneity across and within industries. Our baseline Occupational Emissions Score (OES), along with wage- and concentration-adjusted variations (WOES and COES), highlights substantial differences in emissions exposure across the U.S. workforce. Applying these measures, we document several new facts: emissions are highly concentrated in a small set of occupations; emissions intensity has declined over time; and even within industries, workers' exposure varies significantly by occupation. Higher-emission occupations are disproportionately held by older, male, native-born, and less-educated workers, and are concentrated in particular regions. While higher-emission occupations tend to experience lower employment growth, they show higher hourly wages and vacancy growth. An event study of coal mine closures further shows that high-emission occupations are more exposed to structural shocks. Together, our measures provide a comprehensive, granular framework for understanding occupational risk and adjustment during major economic shifts. |
| Keywords: | occupations; Emissions; labor market dynamics; Coal; energy |
| JEL: | J23 J24 J62 Q52 Q54 R11 |
| Date: | 2025–07–01 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedkrw:101703 |
| By: | Okan Akarsu |
| Abstract: | In this paper, I explore the spillover effects of frontier firms on other firms in Türkiye, using a detailed administrative dataset with firm-level data on balance sheets, inter-firm transactions, and employment. I review key production function estimators, evaluate their assumptions and performance using a large dataset of Turkish firms, and apply estimated productivity to identify frontier firms and assess their influence on laggard firms' performance. Additionally, I contribute to the empirical literature by exploring the spillover and network effects of frontier firms on laggard firms, as well as examining the productivity convergence of laggard firms to frontier firms. The analysis reveals three key findings: (i) Frontier firms generate positive spillover effects within sectors, which enhance sales, employment, exports, and asset growth among laggard firms; (ii) detailed firm-to-firm invoice data reveals that a higher share of frontier firms in a firm’s network significantly boosts investment, net sales, and productivity growth; and (iii) laggard firms show faster productivity growth, with substantial variation across firm types and industries. |
| Keywords: | Spillover effect, Frontier firm, Total factor productivity, Production function estimation, Semiparametric estimator, Laggard firm dynamics |
| JEL: | C13 C14 C23 D24 D40 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:tcb:wpaper:2511 |
| By: | Coppens, Léo; Dietz, Simon; Venmans, Frank |
| Abstract: | We analyse the large and diverse literature on technical change in integrated assessment models (IAMs) of climate change, with a view to understanding how different representations of technical change affect optimal climate policy. We first solve an analytical IAM that features several models of technical change from the literature, including exogenous technical change in abatement technologies, exogenous decarbonisation of the economy, endogenous technical change via learning-by-doing, and endogenous technical change via R&D (in particular, directed technical change). We show how these models of technical change impact optimal carbon prices, emissions and temperatures in often quite different ways. We then survey how technical change is currently represented in the main quantitative IAMs used to inform policy, demonstrating that a range of approaches are used. Exogenous technical change in abatement technologies and learning-by-doing are most popular, although the latter mechanism is only partially endogenous in some models. We go on to quantify technical change in these policy models using structural estimation, and simulate our analytical IAM numerically, assessing the effect of technical change on optimal climate policy. We find large quantitative effects of technical change and large quantitative differences between different representations of technical change, both under cost-benefit and cost-effectiveness objectives. |
| Keywords: | climate change; cost-benefit analysis; directed technical change; induced innovation; integrated assessment models; learning-by-doing; technical change |
| JEL: | C61 O30 Q54 Q55 Q58 |
| Date: | 2025–09–30 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:129025 |
| By: | Catherine Wu; Arun Sundararajan |
| Abstract: | Generative AI is a technology which depends in part on participation by humans in training and improving the automation potential. We focus on the development of an "AI twin" that could complement its creator's efforts, enabling them to produce higher-quality output in their individual style. However, AI twins could also, over time, replace individual humans. We analyze this trade-off using a principal-agent model in which agents have the opportunity to make investments into training an AI twin that lead to a lower cost of effort, a higher probability of success, or both. We propose a new framework to situate the model in which the tasks performed vary in the ease to which AI output can be improved by the human (the "editability") and also vary in the extent to which a non-expert can assess the quality of output (its "verifiability.") Our synthesis of recent empirical studies indicates that productivity gains from the use of generative AI are higher overall when task editability is higher, while non-experts enjoy greater relative productivity gains for tasks with higher verifiability. We show that during investment a strategic agent will trade off improvements in quality and ease of effort to preserve their wage bargaining power. Tasks with high verifiability and low editability are most aligned with a worker's incentives to train their twin, but for tasks where the stakes are low, this alignment is constrained by the risk of displacement. Our results suggest that sustained improvements in company-sponsored generative AI will require nuanced design of human incentives, and that public policy which encourages balancing worker returns with generative AI improvements could yield more sustained long-run productivity gains. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.08732 |
| By: | E. Marrocu; R. Paci; L. Serafini |
| Abstract: | This paper investigates the impact of digital and green programmes within Smart Specialisation Strategies on regional productivity growth across European regions. It examines the combined influence of digital and green priorities (Twin Transition) and how their effects vary according to regions' initial economic conditions. The analysis reveals a U-shaped relationship - the Twin Transition is positively and significantly associated with productivity growth in low-productivity regions, whereas regions with intermediate productivity levels exhibit weaker or even negative associations. Conversely, high-productivity regions experience modest yet stabilising effects. These findings highlight the significance of the middle-income trap and the need for context-sensitive policy design. |
| Keywords: | Green policies;Digital policies;Twin Transition;Smart Specialisation Strategy;regional economic growth;european regions |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:cns:cnscwp:202510 |